A response-surface approach into the interactive effects of multiple stressors reveals new insights into complex responses
Bibliographic record
Abstract
Understanding the difficult to predict interactive effects of anthropogenic stressors is recognized as one of the major challenges facing environmental scientists and ecosystem managers. Despite burgeoning research, predicting stressor interactions is still difficult, in part because the same two stressors can interact, or not, depending on their intensities. While laboratory experiments have provided useful insights about how organisms respond to serial doses of single stressors, we lack ‘response-surface’ field experiments in which naturally occurring assemblages are exposed to multiple types and concentrations of stressors. Here we used a field-based dosing system combined with a ‘response-surface’ design to test the individual and combined effects of two stressors (copper and chlorpyrifos) at five concentrations of each, for a total of 25 replicated treatments (n=4). After six weeks of dosing, chemical uptake and impacts at several levels of biological organization in mussel assemblages were measured. Stressor combinations produced interactive effects that would not have been revealed without using this replicated ‘response-surface approach’. Results show that non-additive effects of multiple stressors may be more complex and more common than previously thought. Additionally, our findings suggest that interactive effects of multiple stressors vary across levels of organization which has implications for monitoring and managing the chemical, biological and ecological impacts of priority pollutants in the real world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".